Lots of people predict that automated vehicles used to support robotaxis will be more affordable for customers than human drivers and ridesharing.
In some cases, perhaps yes. In other cases, perhaps no.
source: BCG
The simple logic is that since human driver wages can account for 60 percent or more of ride-hailing operational costs, automated vehicle fleets could reduce supplier costs, and lead to lower consumer pricing.
Projections from investment analyses suggest that at scale, robotaxi fares could drop to as low as $0.25-$0.50 per mile, undercutting the $0.70 per mile for personal car ownership and the $2 to $3 per mile for current UberX rides.
Ark Invest certainly agrees with the thesis.
But, for the moment, it depends.
In San Francisco, Waymo rides have averaged 31 percent to 41 percent more than comparable Uber or Lyft trips, though Tesla's early robotaxi offerings have come in cheaper at around $8.17 per ride on average. Other studies suggest the opposite.
In principle, some scenarios seem to support the argument for lower autonomous vehicle costs, with the possibility that rider fares could be lower.
Robotaxis can achieve cost advantages through automation's core efficiencies:
That might be true especially for:
High-density urban areas with strong demand: In cities like San Francisco or Austin, where rides are frequent and vehicles can minimize idle time
Long-distance or high-utilization trips: For routes over 10-20 miles, robotaxis avoid human limitations like breaks or shift changes, potentially reducing costs by 50% or more over time
Projections indicate profitability at $0.50 per mile within 4-5 years, making them cheaper than personal cars for families driving 10,000 miles annually (saving ~$5,000/year)
Subsidized rollouts. Companies like Tesla are initially undercutting competitors with aggressive pricing to gain market share, similar to Uber's early strategies
Electrification and scale economies. Fully electric fleets reduce fuel costs dramatically, and as adoption grows (potentially doubling global miles traveled by 2030), per-ride overheads like insurance and maintenance dilute. McKinsey estimates a 50-percent drop in per-mile costs by 2030 in these optimized setups.
In other cases, the opposite might happen, as human driver services have advantages over robotaxis:
Early deployment or low-demand Areas. In nascent markets or suburban/rural zones with sparse rides, vehicles sit idle more, spreading fixed costs (e.g., $0.30-$0.50 per mile for operations) over fewer trips. Waymo's San Francisco rides average $20.43 vs. $14-15 for Lyft/Uber, a premium driven by expensive hardware and limited scale. During rush hours, inefficiencies like cautious driving add $9-11 extra compared to human services.
Short trips or inefficient routing. For distances under two miles, robotaxis can charge disproportionately more per kilometer due to minimum fares, detours for safety, or slower responses to traffic.
Premium or safety-focused services. Some riders pay more for the novelty or perceived safety. In areas with bad weather, complex traffic, or high accident risks, added insurance and maintenance could keep fares elevated. Regulatory requirements for human oversight (e.g., remote monitoring) also add labor costs, keeping robotaxis pricier than unsupervised human drives in the near term
Monopolistic or regulated markets. If a single provider dominates, they might price at $0.50 per mile for higher margins rather than passing all savings to riders. Local regulations or other offsetting forces, such as strong union opposition, also might have an effect.
So potential prices for riders might vary: higher for some use cases; lower for others.
